高效推理：专注于低延迟和 高吞吐量，适合实时应用。  轻量化设计：模型结构优化， 资源占用少，适合边缘设备 和移动端。  多任务支持：支持多种任务， 如文本生成、分类和问答。 Kimi k1.5  垂直领域优化：针对特定领域 （如医疗、法律）进行优化， 提供高精度结果。  长文本处理：擅长处理长文本 和复杂文档，适合专业场景。  定制化能力：支持用户自定义 训练和微调，适应特定需求。 测试结果受到数据样本、测试环境、AI抽卡、提示词模板等因素影响，仅供参考，无法作为决策制定、质量评估或产品验证的最终依据。 爬虫数据采集  目前DeepSeek R1、Open AI o3mini、Kimi k1.5支持联网查询网址，Claude 3.5 sonnet暂不支持；  四个模型均能根据上传的网页代码，对多个网址链接进行筛选、去重，完全提取出符合指令要求的所有网址链接并形成列表；  在复杂爬虫任务上，DeepSeek o3min生成的代码均能正常执行数据采集任务，o3响应速度更快，R1数据采集结果更加完 整准确；其他2个模型都存在多次调试但代码仍然运行不成功的问题，如代码中罗列URL不全、输出文本中提取数据为空等。 Kimi k1.5 能够提取所有网址，代码运 行后生成本地文件，但提取 数据结果为空。 结论 Claude 3.5 sonnet 可以提取所有网址，调整后可输出正 确代码，运行代码能生成本地文件， 但提取数据结果为空。

Float32 julia> 2.5f-4 0.00025f0 Values can be converted to Float32 easily: julia> x = Float32(-1.5) -1.5f0 julia> typeof(x) Float32 Hexadecimal floating-point literals are also valid, but only as Float64 cleaner:CHAPTER 4. INTEGERS AND FLOATING-POINT NUMBERS 24 julia> x = 3 3 julia> 2x^2 - 3x + 1 10 julia> 1.5x^2 - .5x + 1 13.0 It also makes writing exponential functions more elegant: julia> 2^2x 64 The with the equivalent E form. • The 32-bit floating-point literal expression 1.5f22 could be interpreted as the numeric literal 1.5 multiplied by the variable f22. In all cases the ambiguity is resolved

Float32 julia> 2.5f-4 0.00025f0 Values can be converted to Float32 easily: julia> x = Float32(-1.5) -1.5f0 julia> typeof(x) Float32 Hexadecimal floating-point literals are also valid, but only as Float64 julia> x = 3 3 julia> 2x^2 - 3x + 1CHAPTER 4. INTEGERS AND FLOATING-POINT NUMBERS 24 10 julia> 1.5x^2 - .5x + 1 13.0 It also makes writing exponential functions more elegant: julia> 2^2x 64 The with the equivalent E form. • The 32-bit floating-point literal expression 1.5f22 could be interpreted as the numeric literal 1.5 multiplied by the variable f22. In all cases the ambiguity is resolved

AND FLOATING-POINT NUMBERS 21 Values can be converted to Float32 easily: julia> x = Float32(-1.5) -1.5f0 julia> typeof(x) Float32 Hexadecimal floating-point literals are also valid, but only as Float64 makes writing polynomial expressions much cleaner: julia> x = 3 3 julia> 2x^2 - 3x + 1 10 julia> 1.5x^2 - .5x + 1 13.0 It also makes writing exponential functions more elegant: julia> 2^2x 64 The with the equivalent E form. • The 32-bit floating-point literal expression 1.5f22 could be interpreted as the numeric literal 1.5 multiplied by the variable f22. In all cases the ambiguity is resolved

AND FLOATING-POINT NUMBERS 21 Values can be converted to Float32 easily: julia> x = Float32(-1.5) -1.5f0 julia> typeof(x) Float32 Hexadecimal floating-point literals are also valid, but only as Float64 makes writing polynomial expressions much cleaner: julia> x = 3 3 julia> 2x^2 - 3x + 1 10 julia> 1.5x^2 - .5x + 1 13.0 It also makes writing exponential functions more elegant: julia> 2^2x 64 The with the equivalent E form. • The 32-bit floating-point literal expression 1.5f22 could be interpreted as the numeric literal 1.5 multiplied by the variable f22.CHAPTER 5. INTEGERS AND FLOATING-POINT NUMBERS

AND FLOATING-POINT NUMBERS 21 Values can be converted to Float32 easily: julia> x = Float32(-1.5) -1.5f0 julia> typeof(x) Float32 Hexadecimal floating-point literals are also valid, but only as Float64 makes writing polynomial expressions much cleaner: julia> x = 3 3 julia> 2x^2 - 3x + 1 10 julia> 1.5x^2 - .5x + 1 13.0 It also makes writing exponential functions more elegant: julia> 2^2x 64 The with the equivalent E form. • The 32-bit floating-point literal expression 1.5f22 could be interpreted as the numeric literal 1.5 multiplied by the variable f22.CHAPTER 5. INTEGERS AND FLOATING-POINT NUMBERS

AND FLOATING-POINT NUMBERS 21 Values can be converted to Float32 easily: julia> x = Float32(-1.5) -1.5f0 julia> typeof(x) Float32 Hexadecimal floating-point literals are also valid, but only as Float64 makes writing polynomial expressions much cleaner: julia> x = 3 3 julia> 2x^2 - 3x + 1 10 julia> 1.5x^2 - .5x + 1 13.0 It also makes writing exponential functions more elegant: julia> 2^2x 64 The with the equivalent E form. • The 32-bit floating-point literal expression 1.5f22 could be interpreted as the numeric literal 1.5 multiplied by the variable f22.CHAPTER 5. INTEGERS AND FLOATING-POINT NUMBERS

AND FLOATING-POINT NUMBERS 21 Values can be converted to Float32 easily: julia> x = Float32(-1.5) -1.5f0 julia> typeof(x) Float32 Hexadecimal floating-point literals are also valid, but only as Float64 makes writing polynomial expressions much cleaner: julia> x = 3 3 julia> 2x^2 - 3x + 1 10 julia> 1.5x^2 - .5x + 1 13.0 It also makes writing exponential functions more elegant: julia> 2^2x 64 The with the equivalent E form. • The 32-bit floating-point literal expression 1.5f22 could be interpreted as the numeric literal 1.5 multiplied by the variable f22.CHAPTER 5. INTEGERS AND FLOATING-POINT NUMBERS

AND FLOATING-POINT NUMBERS 21 Values can be converted to Float32 easily: julia> x = Float32(-1.5) -1.5f0 julia> typeof(x) Float32 Hexadecimal floating-point literals are also valid, but only as Float64 makes writing polynomial expressions much cleaner: julia> x = 3 3 julia> 2x^2 - 3x + 1 10 julia> 1.5x^2 - .5x + 1 13.0 It also makes writing exponential functions more elegant: julia> 2^2x 64 The with the equivalent E form. • The 32-bit floating-point literal expression 1.5f22 could be interpreted as the numeric literal 1.5 multiplied by the variable f22.CHAPTER 5. INTEGERS AND FLOATING-POINT NUMBERS

AND FLOATING-POINT NUMBERS 21 Values can be converted to Float32 easily: julia> x = Float32(-1.5) -1.5f0 julia> typeof(x) Float32 Hexadecimal floating-point literals are also valid, but only as Float64 makes writing polynomial expressions much cleaner: julia> x = 3 3 julia> 2x^2 - 3x + 1 10 julia> 1.5x^2 - .5x + 1 13.0 It also makes writing exponential functions more elegant: julia> 2^2x 64 The with the equivalent E form. • The 32-bit floating-point literal expression 1.5f22 could be interpreted as the numeric literal 1.5 multiplied by the variable f22.CHAPTER 5. INTEGERS AND FLOATING-POINT NUMBERS